Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining.
This paper proposes an efficient hash table based closed frequent itemsets (HCFI) mining algorithm to envisage coronary artery disease early. HCFI algorithm generates closed frequent itemsets efficiently by performing intersection operation on transaction id's of itemset without considering the name...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 5; pp. 749 - 760 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=129156209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129156209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2018 vid: 56 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 129156209 129156209 NLM28905236 10.1007/s11517-017-1719-6 NLM28905236 129156209 ppf: 749 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining. aug: au: Dhanaseelan, Ramesh Jeya Sutha, M. affil: Department of Computer Applications, St.Xavier’s Catholic College of Engineering, Chunkankadai, K.K. Dist., 629003, Nagercoil, Tamil Nadu, India sug: subj: Algorithms Coronary Arteriosclerosis Diagnosis Data Mining Databases Female Male Clinical Assessment Tools Exercise of Self-Care Agency Scale Female Male ab: This paper proposes an efficient hash table based closed frequent itemsets (HCFI) mining algorithm to envisage coronary artery disease early. HCFI algorithm generates closed frequent itemsets efficiently by performing intersection operation on transaction id's of itemset without considering the name of item/itemset. The employed hash table reduces search efficiency to O(1) or constant time. HCFI algorithm is applied on the UCI (University of California, Irvine) Cleveland dataset, a biological database of cardiovascular disease to generate closed frequent itemsets on the dataset. The findings of HCFI algorithm are (1) it determines a set of distinguished features to differentiate a 'healthy' and a 'sick' class. The features such as heart status being normal, oldpeak being less than or equal to 1.2, slope being up, number of vessels colored being zero, absence of exercise-induced angina, maximum heart rate achieved between 151 and 180 are referred as 'healthy' class. The features like chest pain are being asymptomatic, heart-status being reversible defect, slope being flat, and presence of exercise-induced-angina and serum cholesterol being greater than 240 indicate a presumption of heart disease to both genders. (2) It predicts that females have less chance of coronary heart disease than males. This algorithm is also compared with two other state-of-the-art-algorithms 'NAFCP' (N-list based algorithm for mining frequent closed patterns) and 'PredictiveApriori' to show the effectiveness of the proposed algorithm. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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